[1] Chun Q, Van B K, Pan J W, et al. Structural performance and repair methodology of the Wenxing lounge bridge in China [J]. International Journal of Architectural Heritage, 2015, 9: 730-743. doi:  10.1080/15583058.2015.1041191
[2] Li H Q, Yu Y, Yu X. On fire protection problems and its countermeasures about Chinese ancient architecture [J]. Applied Mechanics and Materials, 2012, 204-208: 3365-3368.
[3] Murphy M, McGovern E, Pavia S. Historic building information modelling-Adding intelligence to laser and image based surveys of European classical architecture [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 76: 89-102.
[4] Wang M. Research on the application of three-dimensional laser scanning technology in the surveying and mapping of ancient buildings in the Forbidden City [J]. Palace Museum Journal, 2011, 6: 143-156. (in Chinese)
[5] Song L, Li X, Yang Y G, et al. Structured-light based 3D reconstruction system for cultural relic packaging [J]. Sensors, 2018, 18(9): 2981. doi:  10.3390/s18092981
[6] Shao H, Chen Y W, Jiang C H, et al. Feasibility study on hyperspectral LiDAR for ancient Huizhou-style architecture preservation [J]. Remote Sensing, 2020, 12(1): 88. doi:  https://doi.org/10.3390/rs12010088
[7] Yang Y, Jiang X T, Kuang Y H. Arithmetic of 3-D house reconstruction based on graphics understanding [J]. Information and Electronic Engineering, 2011, 9(1): 105-108. (in Chinese)
[8] Sun Z, Cao Y K, Zhang Y Y. Applications of image-based modeling in architectural heritage surveying [J]. Research on Heritage and Preservation, 2018(1): 30-36. (in Chinese)
[9] Ivan A M, Luigi B, Marco S, et al. Mapping infrared data on terrestrial laser scanning 3D models of buildings [J]. Remote Sensing, 2011, 3(9): 1847-1870. doi:  10.3390/rs3091847
[10] Costanzo A, Minasi M, Casula G, et al. Combined use of terrestrial laser scanning and IR thermography applied to a historical building [J]. Sensors, 2015, 15(1): 194-213. doi:  https://doi.org/10.3390/s150100194
[11] Murtiyoso A, Grussenmeyer P, Suwardhi D, et al. Multi-scale and multi-sensor 3D documentation of heritage complexes in urban areas [J]. ISPRS International Journal of Geo-Information, 2018, 7(12): 483. doi:  https://doi.org/10.3390/ijgi7120483
[12] Hu Q W, Wang S H, Fu C W, et al. Fine surveying and 3D modeling approach for wooden ancient architecture via multiple laser scanner integration [J]. Remote Sensing, 2016, 8(4): 270. doi:  10.3390/rs8040270
[13] Kaasalainen S, Lindroos T, Hyyppä J. Toward hyperspectral lidar: Measurement of spectral backscatter intensity with a supercontinuum laser source [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(2): 211-215. doi:  10.1109/LGRS.2006.888848
[14] Chen Y W, Räikkönen E, Kaasalainen S, et al. Two-channel hyperspectral LiDAR with a supercontinuum laser source [J]. Sensors, 2010, 10(7): 7057-7066. doi:  https://doi.org/10.3390/s100707057
[15] Chen Y W, Jiang C H, Hyyppä J, et al. Feasibility study of ore classification using active hyperspectral LiDAR [J]. IEEE Geoscience & Remote Sensing Letters, 2018, 15(11): 1785-1789.
[16] Shao H, Chen Y W, Jiang C H, et al. A 91-channel hyperspectral LiDAR for coal/rock classification [J]. IEEE Geosci Remote Sensing Lett, 2020, 76(6): 1052-1056.
[17] Jiang C H, Chen Y W, Tian W X, et al. A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM [J]. Satellite Navigation, 2020, 1: 29. doi:  https://doi.org/10.1186/s43020-020-00029-5
[18] 何子辛, 邵慧, 郭航, 等. 基于高光谱激光雷达信号强度免校准的煤岩分类[J]. 红外与激光工程, 2021, 50(10): 154-162. doi:  10.3788/IRLA20200518

He Z X, Shao H, Guo H, et al. Classification of coal/rock based on hyperspectral LiDAR calibration-free signals [J]. Infrared and Laser Engineering, 2021, 50(10): 20200518. (in Chinese) doi:  10.3788/IRLA20200518
[19] Gebhart S C, Stokes D L, Vodinh T, et al. Instrumentation considerations in spectral imaging for tissue demarcation: comparing three methods of spectral resolution[C]//Proceedings of SPIE, 2005, 5694: 41-52.
[20] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in python [J]. Journal of Machine Learning Research, 2011, 12: 2825-2830. doi:  10.5555/1953048.2078195